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International audience; The detection of immunoglobulin G (IgG) oligoclonal bands (OCB) in cerebrospinal fluid (CSF) by isoelectric focusing (IEF) is a valuable tool for the diagnosis of multiple sclerosis. Over the last decade, the results of our clinical research have suggested that tears are a non-invasive alternative to CSF. However, since tear samples have a lower IgG concentration than CSF, a sensitive OCB detection is therefore required. We are developing the first automatic tool for IEF analysis, with a view to speeding up the current visual inspection method, removing user variability, reducing misinterpretation, and facilitating OCB quantification and follow-up studies. The removal of band distortion is a key image enhancement step in increasing the reliability of automatic OCB detection. Here, we describe a novel, fully automatic band-straightening algorithm. The algorithm is based on a correlation directional warping function, estimated using an energy minimization procedure. The approach was optimized via an innovative coupling of a hierarchy of image resolutions to a hierarchy of transformation, in which band misalignment is corrected at successively finer scales. The algorithm’s performance was assessed in terms of the bands’ standard deviation before and after straightening, using a synthetic dataset and a set of 200 lanes of CSF, tear, serum and control samples on which experts had manually delineated the bands. The number of distorted bands was divided by almost 16 for the synthetic lanes and by 7 for the test dataset of real lanes. This method can be applied effectively to different sample types. It can realign minimal contrast bands and is robust for non-uniform deformations.

We investigate a problem in which each member of a group of learners is trained separately to solve the same classification task. Each learner has access to a training dataset (possibly with overlap across learners) but each trained classifier can be evaluated on a validation dataset. We propose a new approach to aggregate the learner predictions in the possibility theory framework. For each classifier prediction, we build a possibility distribution assessing how likely the classifier prediction is correct using frequentist probabilities estimated on the validation set. The possibility distributions are aggregated using an adaptive t-norm that can accommodate dependency and poor accuracy of the classifier predictions. We prove that the proposed approach possesses a number of desirable classifier combination robustness properties. Moreover, the method is agnostic on the base learners, scales well in the number of aggregated classifiers and is incremental as a new classifier can be appended to the ensemble by building upon previously computed parameters and structures. A python implementation can be downloaded at this link https://github.com/john-klein/SPOCC .

The latest revision of multiple sclerosis diagnosis guidelines emphasizes the role of oligoclonal band detection in isoelectric focusing images of cerebrospinal fluid. Recent studies suggest tears as a promising noninvasive alternative to cerebrospinal fluid. We are developing the first automatic method for isoelectric focusing image analysis and oligoclonal band detection in cerebrospinal fluid and tear samples. The automatic analysis would provide an accurate, fast analysis and would reduce the expert-dependent variability and errors of the current visual analysis. In this paper, we describe a new effective model for the fully automated segmentation of highly distorted lanes in isoelectric focusing images. This approach is a new formulation of the classic parametric active contour problem, in which an open active contour is constrained to move from the top to the bottom of the image, and the x-axis coordinate is expressed as a function of the y-axis coordinate. The left and right edges of the lane evolved together in a ribbon-like shape so that the full width of the lane was captured reliably. The segmentation algorithm was implemented using a multiresolution approach in which the scale factor and the active contour control points were progressively increased. The lane segmentation algorithm was tested on a database of 51 isoelectric focusing images containing 419 analyzable lanes. The new model gave robust results for highly curved lanes, weak edges, and low-contrast lanes. A total of 98.8% of the lanes were perfectly segmented, and the remaining 1.2% had only minor errors. The computation time (1 s per membrane) is negligible. This method precisely defines the region of interest in each lane and thus is a major step toward the first fully automatic tool for oligoclonal band detection in isoelectric focusing images. Graphical abstract.

International audience; Localizing the tampered regions in forgery images is an important and challenging problem in forensics applications. Though there have been an extensive studies on image forgery localization over past decade, each method still has its own limitations. Therefore, it is promising to fuse different forensic approaches in order to obtain better localization performance. In this paper, we propose a framework to aggregate the decision maps of two forensic approaches: Photo Response Non-Uniformity (PRNU) based approach and statistical features based approach using Dempster-Shafer Theory. PRNU noise can be considered as a camera fingerprint thereby being used effectively to localize tampering images. However, the most challenging limitation of this approach is its false identifications on textured, saturated and dark regions. By combining with the statistical feature based approach, we can decrease this false alarm rate on saturated and dark regions. The extensive experimental results demonstrate that the proposed method significantly outperforms the single PRNU based approach.

National audience; Cover-Source Mismatch (CSM) has long been identified as one of the most important issues for hidden information detection. Recent works have shown that the CSM finds its roots mainly in the image acquisition and processing pipeline. This short paper builds on those recent research results by comparing two approaches for mitigating the CSM using the knowledge of the image processing pipeline. In particular, we have developed an efficient methodology for identifying the differeent processing pipelines used to generate a dataset. Based on this identification method we experimentally show that it is possible to set the steganalysis problem almost free from CSM using either a targeted approach, tailored to each and every processing pipeline, or diversifying widely the training dataset.; Le Cover-Source Mismatch (CSM, ou inadéquation de la source d'images de couverture) a été depuis bien longtemps identifié comme l'un des problèmes les plus importants pour la détection d'information cachée. Des travaux récents ont montré que les causes du CSM sont principalement dues à la chaîne de traitement et relativement peu de la chaîne d'acquisition. Ce court papier propose de tirer parti de ces résultats en comparant expérimentalement deux approches pour lutter contre le CSM en utilisant la connaissance de la chaîne de traitement. Nous développons en particulier une méthodologie efficace pour identifier les différentes chaînes de traitements composant une base d'image. À partir de cette méthode d'identification, nous montrons expérimentalement qu'il est possible de retrouver un cadre où le CSM est quasi-absent soit en effectuant une stéganalyse ciblée sur chaque chaîne de traitement, soit en diversifiant suffisamment la base d'apprentissage.

International audience; This paper presents ins and outs of the ALASKA challenge, a ste-ganalysis challenge built to reflect the constraints of a forensic steganalyst. We motivate and explain the main differences w.r.t. the BOSS challenge (2010), specifically the use of a ranking metric prescribing high false positive rates, the analysis of a large diversity of different image sources and the use of a collection of steganographic schemes adapted to handle color JPEGs. The core of the challenge is also described, this includes the RAW image data-set, the implementations used to generate cover images and the specificities of the embedding schemes. The very first outcomes of the challenge are then presented, and the impacts of different parameters such as demosaicking, filtering, image size, JPEG quality factors and cover-source mismatch are analyzed. Eventually, conclusions are presented, highlighting positive and negative points together with future directions for the next challenges in practical steganalysis.

We evaluated the feasibility of using the kinetic of diffusion-weighted MRI (DWI) and the normalized apparent coefficient diffusion (ADC) map value as an early biomarker in patients treated by external beam radiotherapy (EBRT). Twelve patients were included within the frame of a multicenter phase II trial and treated for intermediate risk prostate cancer (PCa). Multiparametric MRI was performed before treatment (M0) and every 6 months until M24. Association between nADC and PSA or PSA kinetic was evaluated using the test of nullity of the Spearman correlation coefficient. The median rates of PSA at the time of diagnosis, two years and four years after EBRT were 9.29 ng/ml (range from 5.26 to 17.67), 0.68 ng/ml (0.07–2.7), 0.47 ng/ml (0.09–1.39), respectively. Median nADC increased from 1.14 × 10−3 mm2/s to 1.59 × 10−3 mm2/s between M0 and M24. Only one patient presented a decrease of nADC (1.35 × 10−3 mm2/s and 1.11 × 10−3 mm2/s at M0 and M12 respectively). The increase in nADC at M6 was correlated with PSA decrease at M18, M24 and M30 (p

International audience; With progress in medical genetics, genome-sequencing techniques are becoming more and more efficient. However, these genetic tests may lead to the detection of unsolicited genetic findings, i.e. findings that are not the primary purpose of the screening. New ethical issues have emerged, in particular the question of whether to disclose these unsolicited findings to the patient or not. Forty-seven patients under supervision in a Medical Genetics service, 15 health professionals and 107 members of the French general population expressed their opinion regarding the appropriateness of disclosing an unsolicited high penetrance genetic finding in 36 scenarios containing three pieces of information on: a) patient information and consent;b) possibility of prevention and treatment of the detected genetic disease; and c) disclosure of the results by the physician (e.g., no disclosure of the unsolicited results). Four positions were found that were called Respect for patient’s autonomy, Beneficence to patient, Non-maleficence, and Always appropriate.; Con el progreso en medicina genética, las técnicas de secuencias de genomas están volviéndose más eficientes. Sin embargo, estos test genéticos pueden llevar a la detección de hallazgos no solicitados, e.g., hallazgos que no son el propósito primario del escaneo. Nuevos problemas éticos han surgido, en particular la pregunta de si revelar o no estos hallazgos no solicitados al paciente. 47 Pacientes bajo supervisión en un servicio de Medicina Genética, 15 profesionales de la salud y 107 miembros de la población general francesa, expresaron su opinión respecto a lo apropiado de revelar un hallazgo genético no solicitado de alto efecto en 36 escenarios con 3 piezas de información sobre: a) información del paciente y consentimiento, b) posibilidad de prevención y tratamiento de la enfermedad genética detectada, y c) revelación de los resultados por parte del médico (e.g., no revelar de los resultados no solicitados). Se encontraron cuatro posiciones que fueron llamadas Respeto por la autonomía del paciente, Beneficencia al paciente, No-maleficencia, y Siempre apropiado.

International audience; With progress in medical genetics, genome-sequencing techniques are becoming more and more efficient. However, these genetic tests may lead to the detection of unsolicited genetic findings, i.e. findings that are not the primary purpose of the screening. New ethical issues have emerged, in particular the question of whether to disclose these unsolicited findings to the patient or not. Forty-seven patients under supervision in a Medical Genetics service, 15 health professionals and 107 members of the French general population expressed their opinion regarding the appropriateness of disclosing an unsolicited high penetrance genetic finding in 36 scenarios containing three pieces of information on: a) patient information and consent;b) possibility of prevention and treatment of the detected genetic disease; and c) disclosure of the results by the physician (e.g., no disclosure of the unsolicited results). Four positions were found that were called Respect for patient’s autonomy, Beneficence to patient, Non-maleficence, and Always appropriate.; Con el progreso en medicina genética, las técnicas de secuencias de genomas están volviéndose más eficientes. Sin embargo, estos test genéticos pueden llevar a la detección de hallazgos no solicitados, e.g., hallazgos que no son el propósito primario del escaneo. Nuevos problemas éticos han surgido, en particular la pregunta de si revelar o no estos hallazgos no solicitados al paciente. 47 Pacientes bajo supervisión en un servicio de Medicina Genética, 15 profesionales de la salud y 107 miembros de la población general francesa, expresaron su opinión respecto a lo apropiado de revelar un hallazgo genético no solicitado de alto efecto en 36 escenarios con 3 piezas de información sobre: a) información del paciente y consentimiento, b) posibilidad de prevención y tratamiento de la enfermedad genética detectada, y c) revelación de los resultados por parte del médico (e.g., no revelar de los resultados no solicitados). Se encontraron cuatro posiciones que fueron llamadas Respeto por la autonomía del paciente, Beneficencia al paciente, No-maleficencia, y Siempre apropiado.

This work presents the current state-of-the-art in techniques for tracking a number of objects moving in a coordinated and interacting fashion. Groups are structured objects characterized with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport players, convoy of cars) or of hundreds or thousands of components such as crowds of people. The group object tracking is closely linked with extended object tracking but at the same time has particular features which differentiate it from extended objects. Extended objects, such as in maritime surveillance, are characterized by their kinematic states and their size or volume. Both group and extended objects give rise to a varying number of measurements and require trajectory maintenance. An emphasis is given here to sequential Monte Carlo (SMC) methods and their variants. Methods for small groups and for large groups are presented, including Markov Chain Monte Carlo (MCMC) methods, the random matrices approach and Random Finite Set Statistics methods. Efficient real-time implementations are discussed which are able to deal with the high dimensionality and provide high accuracy. Future trends and avenues are traced. © 2013 Elsevier Inc. All rights reserved.

International audience; Most color cameras are fitted with a single sensor that provides color filter array (CFA) images, in which each pixel is characterized by one of the three color components (either red, green, or blue). To produce a color image, the two missing color components have to be estimated at each pixel of the corresponding CFA image. This process is commonly referred to as demosaicing, and its result as the demosaiced color image. Since demosaicing methods intend to produce "perceptually satisfying" demosaiced color images, they attempt to avoid color artifacts. Because this is often achieved by filtering, demosaicing schemes tend to alter the local texture information that is, however, useful to discriminate texture images. To avoid this issue while exploiting color information for texture classification, it may be relevant to compute texture descriptors directly from CFA images. From chromatic co-occurrence matrices (CCMs) that capture the spatial interaction between color components, we derive new descriptors (CFA CCMs) for CFA texture images. Color textures are then compared by means of the similarity between their CFA CCMs. Experimental results achieved on benchmark color texture databases show the efficiency of this approach for texture classification.

International audience; Grouping 3D-objects into (semantically) meaningful categories is a challenging and important problem in 3D-mining and shape processing. Here, we present a novel approach to categorize 3D-objects. The method described in this paper, is a belief function based approach and consists of two stages. The training stage, where 3D-objects in the same category are processed and a set of representative parts is constructed, and the labeling stage, where unknown objects are categorized. The experimental results obtained on the Tosca- Sumner and the Shrec07 datasets show that the system efficiently performs in categorizing 3D-models.

International audience; Semi-supervised context characterized by the presence of a few pairs of constraints between learning samples is abundant in many real applications. Analysing these instance constraints by recent spectral scores has shown good performances for semi-supervised feature selection. The performance evaluation of these scores is generally based on classification accuracy and is performed in a ground truth context. However, this supervised context used in the evaluation is inconsistent with the semi-supervised context used in the feature selection. In this paper, we propose a semi-supervised performance evaluation procedure, so that both feature selection and clustering take into account the constraints given by the user. In this way, the selection and the evaluation steps are performed in the same context which is close to real life applications. Extensive experiments on benchmark datasets are carried out in the last section. These experiments are performed using a supervised classical evaluation and the semi-supervised proposed one. They demonstrate the effectiveness of feature selection based on constraint analysis that uses both pairwise constraints and the information brought by the unlabeled data.

International audience; Dimensional control of metallic objects is challenging since their surfaces present strong specular properties. The 3D measurement systems based on laser-camera triangulation project the laser on to the surface of the object and do not always provide satisfying results for metallic object. In this paper, we analyze images of metallic objects that are corrupted by speckle noise as a result of their surface aspect problems. The performance reached by those systems depends on the accuracy of extraction of the coordinates of pixels that correspond to the center of the laser line projected on the object. We have developed a column-wise laser stripe extraction method that can be used for industrial application in real-time. To qualify this system, we have made various experiments on synthetic images of these objects, and compare the accuracy of the proposed stripe extraction methods.

International audience

Scheimpflug principle requires that the image sensor, lens, and object planes intersect at a single line known as “Scheimpflug line.” This principle has been employed in several applications to increase the depth of field needed for accurate dimensional measures. In order to provide a metric 3D reconstruction, we need to perform an accurate camera calibration. However, pin-hole model assumptions used by classical camera calibration techniques are not valid anymore for Scheimpflug setup. In this paper, we present a new intrinsic calibration technique using bundle adjustment technique. We elaborate Scheimpflug formation model, and show how we can deal with introduced Scheimpflug distortions, without the need to estimate their angles. The proposed method is based on the use of optical lens distortion models. An experimental comparison of the proposed approach with Scheimpflug model has been made on real industrial data sets under the presence of large distortions. We have shown a slight improvement brought by the proposed method.

Resume Le recalage de donnees d’imagerie medicale multi-modalite est d’une importance capitale dans les techniques d’ablation focalisee du cancer de la prostate. Ces therapies sont souvent guidees par l’imagerie d’echographie, tandis que la planification est effectuee sur les images a resonance magnetique (IRM) preoperatives. Nous proposons une nouvelle methode de recalage geometrique non rigide permettant de recaler les donnees de planification effectuee en IRM a l’echographie de guidage. Notre methode combine un appariement robuste des points marqueurs et le calcul d’une transformation non rigide basee sur les splines de plaque mince. Des resultats preliminaires obtenus sur des images de fantome et de patients montrent que notre methode est precise et robuste face a la variation des conditions de placement des marqueurs.

Resume Les recents progres qu’a connus l’imagerie a resonance magnetique (IRM) ont permis de developper de nouvelles pratiques de diagnostic du cancer de la prostate, dont le principal enjeu est de mieux detecter les tumeurs sur les supports d’imagerie. Dans ce contexte, la delimitation et l’evaluation du volume de la glande sont des operations necessaires souvent effectuees, manuellement, par un operateur expert. Ces tâches sont fastidieuses et demandent un temps d’execution trop important, notamment dans le cas de l’imagerie 3D (IRM, scanner). Elles souffrent aussi du biais relatif a l’operateur, ce qui en reduit la reproductibilite et par consequent la fiabilite. Dans cet article, nous proposons une methode d’extraction automatique des contours de la prostate en IRM, afin d’assister le praticien dans cette tâche et de la rendre moins couteuse dans le contexte de pratique hospitaliere. D’abord, un modele statistique deformable 3D, developpe au sein de l’equipe, est utilise pour obtenir une segmentation initiale. Ensuite, une approche markovienne nous permet d’affiner cette segmentation en se basant sur les intensites de la glande en IRM. La segmentation est alors optimisee par Iterative Conditional Mode (ICM) selon un critere de Maximum A Posteriori. Les resultats de l’evaluation effectuee sur des donnees de patients montrent que la methode developpee est suffisamment precise, rapide et robuste pour pouvoir etre utilisee dans un contexte clinique. Une validation multicentrique et un transfert a l’industrie devraient, a l’avenir, concretiser les retombees cliniques de ce travail.

International audience; L'essor de la radiothérapie conformationnelle et de la radiothérapie guidée par l'image a pour corollaire l'accroissement du temps consacré à la délinéation des volumes d'intérêt, qui est également le siège d'une variabilité inter et intraobservateur. Les développements informatiques et du traitement d'images ont permis des progrès dans l'automatisation partielle ou totale de ces tâches. Cet article détaille les grands principes de la segmentation d'images appliquée à la radiothérapie, ses applications ainsi que ses résultats les plus récents dans un contexte clinique.

International audience

Accurate localization and contouring of prostate are crucial issues in prostate cancer diagnosis and/or therapies. Although several semi-automatic and automatic segmentation methods have been proposed, manual expert correction remains necessary. We introduce a new method for automatic 3D segmentation of the prostate gland from magnetic resonance imaging (MRI) scans. A statistical shape model was used as an a priori knowledge, and gray levels distribution was modeled by fitting histogram modes with a Gaussian mixture. Markov fields were used to introduce contextual information regarding voxels’ neighborhoods. Final labeling optimization is based on Bayesian a posteriori classification, estimated with the iterative conditional mode algorithm. We compared the accuracy of this method, free from any manual correction, with contours outlined by an expert radiologist. In 12 cases, including prostates with cancer and benign prostatic hypertrophy, the mean Hausdorff distance and overlap ratio were 9.94 mm and 0.83, respectively. This new automatic prostate MRI segmentation method produces satisfactory results, even at prostate’s base and apex. The method is computationally feasible and efficient.

National audience; Les interfaces cerveau-ordinateur (BCIs: Brain-Computer Interfaces) utilisent l'activité cérébrale d'un individu pour dialoguer avec un ordinateur. L'aide à la communication (outil d'épellation, interface domotique) et la récupération du mouvement (contrôle d'une prothèse ou d'un robot) sont les applications les plus fréquentes des BCIs dans le domaine de l'assistance. L'étude de cas présentée dans cet article montre que les BCIs peuvent également être utilisées dans une approche thérapeutique par neurofeedback pour la rééducation ou la récupération fonctionnelle. Nous décrivons, dans un premier temps, les utilisations thérapeutiques connues des interfaces cerveau-ordinateur. Puis nous présentons une expérience clinique durant laquelle une BCI a été utilisée comme outil d'aide à la rééducation motrice par un patient atteint d'une hémiparésie du côté droit.

International audience; Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian state-space models. We address here the case where the noise probability density functions are of unknown functional form. A flexible Bayesian nonparametric noise model based on Dirichlet process mixtures is introduced. Efficient Markov chain Monte Carlo and Sequential Monte Carlo methods are then developed to perform optimal batch and sequential estimation in such contexts. The algorithms are applied to blind deconvolution and change point detection. Experimental results on synthetic and real data demonstrate the efficiency of this approach in various contexts.

International audience

One of the most widely used approaches in the context of object recognition across illumination changes consists in comparing the images by means of the intersection between invariant histograms. However, this approach does not provide satisfying results with limited image databases. We propose to cope with the problem of illumination changes by analyzing simultaneously the color components of the pixels and their spatial arrangement in the image. For this purpose, we introduce the chromatic co-occurrence matrices to characterize the relationship between the color component levels of neighboring pixels. In order to compare two images acquired under different illuminations, these matrices are transformed into adapted co-occurrence matrices that are determined so that their intersection is higher when the two images contain the same object lit with different illuminations than when they contain different objects.

International audience; The goal of this paper is to identify the parameter set of a given electrical machine. The identification method is based on data assimilation coupled with FEM model. Data assimilation method is an optimization approach that limits the space of candidate parameter sets by centering it around those of the ideal machine. An application to an electrical machine is presented based on the analysis of flux sensor signals. The methodology is validated by using twin experiments that consider simulated data and tested considering measurements extracted from the ”real” alternator.

International audience; The goal of this paper is to identify rotor inter-turn short-circuits of an alternator. The detection method is based on the analysis of a flux probe signal located in the air gap of the machine. Previous works have shown that pattern recognition can be applied to detect such a fault by using the experimental data as prototypes. A new method is developed here by considering a learning step based on simulation. Therefore the machine is modeled and validated in that purpose. A feature selection is made by considering feature correlation and disparity. Finally, k nearest neighbors is used to classified experimental test data.

International audience; We present the problem of setting up an intrinsic camera calibration under Scheimpflug condition for an industrial application. We aim to calibrate the Scheimpflug camera using a roughly hand positioned calibration pattern with bundle adjustment technique. The assumptions used by classical calibration methodologies are not valid anymore for cameras undergoing Scheimpflug condition. Therefore, we slightly modify pin-hole model to estimate the Scheimpflug angles. The results are tested on real data sets captured from cameras limited by various industrial constraints, and in the presence of large distortions.

International audience; We propose a color-image segmentation algorithm by unsupervised classification of pixels. The originality of the proposed approach consists in iteratively identifying pixel classes by taking into account both the pixel color distributions in several color spaces and their spatial arrangement in the image. In order to overcome the difficult problem of the color space choice, the algorithm selects the color space that is well suited to construct the class at each iteration step. The selection criterion is based on connectedness and color homogeneity measures of pixel subsets. In order to tune the sensitivity of segmentation, we introduce a hierarchical criterion that allows us to segment images with different numbers of regions as human observers do. Experiments carried out on the well-known Berkeley segmentation dataset show that this multicolor space approach succeeds in constructing classes that effectively correspond to regions in the image.

International audience; In this paper, we present a Bayesian approach to accurately track multiple objects based on Received Signal Strength (RSS) measure- ments. This work shows that taking into account the spatial correla- tions of the observations caused by the random shadowing effect can induce significant tracking performance improvements, especially in very noisy scenarios. Additionally, the superiority of the proposed Sequential Markov Chain Monte Carlo (SMCMC) method over the more common Sequential Importance Resampling (SIR) technique is empirically demonstrated through numerical simulations in which multiple targets have to be tracked

International audience; The aim of this article is to develop a GPS/IMU multisensor fusion algorithm, taking context into consideration. Contextual variables are introduced to define fuzzy validity domains of each sensor. The algorithm increases the reliability of the position information.A simulation of this algorithm is then made by fusing GPS and IMU data coming from real tests on a land vehicle. Bad data delivered by GPS sensor are detected and rejected using contextual information thus increasing reliability. Moreover, because of a lack of credibility of GPS signal in some cases and because of the drift of the INS, GPS/INS association is not satisfactory at the moment. In order to avoid this problem, the authors propose to feed the fusion process based on a multisensor Kalman filter directly with the acceleration provided by the IMU. Moreover, the filter developed here gives the possibility to easily add other sensors in order to achieve performances required.

International audience; Copula models have started to be explored in wireless communications, however to date the properties they offer have not been proven or verified on real data experiments. In this paper we provide the first real evidence that the features they offer will provide beneficial modeling capabilities in wireless channel models, which are not just theoretically justified but practically observed. We demonstrate how to utilize the concept of copula modeling in wireless communications. In particular we show for the first time the existence of non-standard dependence features between multiple frequency bands in wireless fading channels using real observations. Classical approaches based on second order statistics are not able to detect such tail dependence features. To model this important feature we fit a mixture copula model based on members of the Archimedean family of copulas to real channel measure- ments. The channel measurements used in this work have been performed at the Barajas airport in Spain, in conjunction with the development of the AeroMACS system for airport surface communications. We give a physical interpretation on this phenomenon and discuss its impact on the design of wireless communication systems with regard to resource allocations.

Impulsive interference mitigation in an OFDM transmission link.Interference is modeled by symmetric alpha stable noise.Joint estimation of multicarrier signal and noise parameters.Bayesian estimation using Sequential Monte Carlo methods.Simulation results demonstrate the efficiency and the robustness of our method. Interference mitigation is one of the main challenges in wireless communication, especially in ad hoc networks. In such context, the Multiple Access Interference (MAI) is known to be of an impulsive nature. Therefore, the conventional Gaussian assumption is inadequate to model this type of interference. Nevertheless, it can be accurately modeled by stable distributions. In fact, it was shown in literature that the α-stable distribution is a useful tool to model impulsive data. In this paper, we tackle the problem of noise compensation in ad hoc networks. More precisely, this issue is addressed within an Orthogonal Frequency Division Multiplexing (OFDM) transmission link assuming a symmetric α-stable model for the signal distortion due to MAI. Based on Bayesian estimation, the proposed approach estimates the transmitted OFDM symbols in the time domain using the Sequential Monte Carlo (SMC) methods. Unlike existing schemes, we consider the more realistic case where the impulsive noise parameters are assumed to be unknown at the receiver. Consequently, our approach deals also with the difficult task of noise parameters estimation which can be very useful for other purposes such as target tracking in wireless sensor networks or channel estimation. Simulations results, provided in terms of Mean Square Error (MSE) and Bit Error Rate (BER), illustrate the efficiency and the robustness of this scheme.

International audience; The images of lace textile are particularly difficult to be analyzed in digital form using classical image processing techniques. The major reasons of this difficulty emerge from the complex nature of lace which generally has different textures in its constituents like the background and patterns. In this paper, we study the behavior of Image Histogram (HistI) and Local Binary Patterns (LBP) on image extracts of lace in presence and absence of rotation. We further evaluate two variants of LBP; primarily the LBP Histogram (LBPB) and secondly the Fourier Transform applied on the LBP Histogram (LBPFFT). Consequently, we analyze the contribution of data fusion on feature level and score level in the different experimentations. The classification rate evaluates the discrimination degree of each descriptor via the k nearest neighbors kNN classifier. Experimental results indicate that the LBPB, LBPFFT and HistI combined at score level generate the better performance in absence of transformations. Whereas, LBPFFT and HistI combined at the same level generate the better classification rate, in the presence of rotation.

International audience; This paper combines the principles of statistical estimation and hypothesis testing to analyse the impact of parameter estimation on an authentication system based on graphical codes. The studied authentication system uses the fact that a code, once printed, undergoes a stochastic and non invertible al- teration. A statistical test applies a likelihood ratio between the model of the authentic printed and scanned image and the model of the reproduced one, with the particularity here that the later model is unknown. The proposed solution consists in using an optimal estimation of the image model coming from observed fake codes in order to perform the likelihood test. Using a second order expansion, we derive a linear re- lation between the quadratic error of the estimated parame- ters and the probability of type II error. We are then able to formulate analytically and practically the error spread region of the Receiver Operating Characteristic (ROC) curves, and to compute the average authentication performance when the receiver has to estimate the opponent print and scan channel.

International audience; In a recent paper [1], we introduced a new family of evidential distances in the framework of belief functions. Using specialization matrices as a representation of bodies of evidence, an evidential distance can be obtained by computing the norm of the difference of these matrices. Any matrix norm can be thus used to define a full metric. In particular, it has been shown that the $L^1$ norm-based specialization distance has nice properties. This distance takes into account the structure of focal elements and has a consistent behavior with respect to the conjunctive combination rule. However, if the frame of discernment on which the problem is defined has $n$ elements, then a specialization matrix size is $2^n \times 2^n$. The straightforward formula for computing a specialization distance involves a matrix product which can be consequently highly time consuming. In this article, several faster computation methods are provided for $L^p$ norm-based specialization distances. These methods are proposed for special kinds of mass functions as well as for the general case.

International audience; This paper presents an adaptive approach based on probabilistic Bayesian inference to estimate the parameters of an atmospheric pollution source term. After introducing the problem and assessing the computational framework, we present an Importance Sampling based algorithm called Adaptive Multiple Importance Sampling (AMIS). It performs an efficient calculation of the source parameter posterior distribution by iteratively upgrading the proposal's parameters and recycling all generations of weighted samples, thus allowing a faster convergence and reducing the number of necessary iterations. We highlight the results of the AMIS by comparing it to a MCMC estimation in a simple example.

International audience; We consider the problem of a set of nodes which is required to collectively learn a common dictionary from noisy measurements. This distributed dictionary learning approach may be useful in several contexts including sensor networks. Dif-fusion cooperation schemes have been proposed to estimate a consensus solution to distributed linear regression. This work proposes a diffusion-based adaptive dictionary learning strategy. Each node receives measurements which may be shared or not with its neighbors. All nodes cooperate with their neighbors by sharing their local dictionary to estimate a common representa-tion. In a diffusion approach, the resulting algorithm corresponds to a distributed alternate optimization. Beyond dictionary learn-ing, this strategy could be adapted to many matrix factorization problems in various settings. We illustrate its efficiency on some numerical experiments, including the difficult problem of blind hyperspectral images unmixing.

International audience; Sequential Monte Carlo (SMC) methods are not only a popular tool in the analysis of state-space models, but offer a powerful alternative to Markov chain Monte Carlo (MCMC) in situations where static Bayesian inference must be performed via simulation. In this paper, we propose a recycling scheme of all past simulated particles in the SMC sampler in order to reduce the variance of the final estimator. We demonstrate how the proposed approach outperforms the classi- cal strategy in two challenging models.

International audience; This paper proposes to investigate the impact of the channel model for authentication systems based on codes that are corrupted by a physically unclonable noise such as the one emitted by a printing process. The core of such a system for the receiver, is to perform a statistical test in order to recognize and accept an original code corrupted by noise and reject any illegal copy or a counterfeit. This study highlights the fact that the probability of type I and type II errors can be better approximated, by several orders of magnitude, when using the Cramér-Chernoff theorem instead of a Gaussian approximation. The practical computation of these error probabilities is also possible using Monte-Carlo simulations combined with the importance sampling method. By deriving the optimal test within a Neyman-Pearson setup, a first theoretical analysis shows that a thresholding of the received code induces a loss of performance. A second analysis proposes to find the best parameters of the channels involved in the model in order to maximize the authentication performance. This is possible when the opponent's channel is identical to the legitimate channel but also when the opponent's channel is different, leading this time to a min-max game between the two players. Finally we evaluate the impact of an uncertainty for the receiver on the opponent channel and we show that the authentication is still possible whenever the receiver can observe forged codes and uses them to estimate the parameters of the model.

International audience; We show that the differential matrix of a uniform homogeneous medium containing birefringence may not be uniquely determined from its Mueller matrix, resulting in the potential existence of an infinite set of elementary polarization properties parameterized by an integer parameter. The uniqueness depends on the symmetry properties of a special differential matrix derived from the eigenvalue decomposition of the Mueller matrix. The conditions for the uniqueness of the differential matrix are identified, physically discussed and illustrated on examples from the literature.

International audience; In this paper, we focus on the challenging task of the online esti- mation of the state and the unknown measurement noise density in nonlinear dynamic state-space models. We are especially interested in making inference in the presence of impulsive and time-varying noise. A flexible Bayesian nonparametric noise model based on an extension of Dirichlet Process Mixtures, namely the Time Varying Dirichlet process Mixtures, is introduced. A novel method based on sequential Monte Carlo methods is proposed to perform the optimal online estimation. Simulation results demonstrate the efficiency and the robustness of this method.

International audience; While the registration step is often problematic for super-resolution, many microscopes and telescopes are now equipped with a piezoelectric mechanical system which permits to ac-curately control their motion (down to nanometers). There-fore one can use such devices to acquire multiple images of the same scene at various controlled positions. Then a fast super-resolution algorithm [1] can be used for efficient super-resolution. However the minimal use of r 2 images for a resolution enhancement factor r is generally not sufficient to obtain good results. We propose to take several images at po-sitions randomly distributed close to each reference position. We study the number of images necessary to control the error resulting from the super-resolution algorithm by [1] due to the uncertainty on positions. The main result is a lower bound on the number of images to respect a given error upper bound with probability higher than a desired confidence level.

International audience; Several image test suites are available in the literature to evaluate the performance of classification schemes. In the framework of colour texture classification, OuTex-TC-00013 (OuTex) and Contrib-TC-00006 (VisTex) are often used. These colour texture image sets have allowed the accuracies reached by many classification schemes to be compared. However, by analysing the classification results obtained with these two sets of colour texture images, we have noticed that the use of colour histogram yields a higher rate of well-classified images compared to colour texture features. It does not take into account any texture information in the image, this incoherence leads us to question the relevance of these two benchmark colour texture sets for measuring the performances of colour texture classification algorithms. Indeed, the partitioning used to build these two sets consists of extracting training and validating sub-images of an original image. We show that such partitioning leads to biased classification results when it is combined with a classifier such as the nearest neighbour. In this paper a new relevant image test suite is proposed for evaluating colour texture classification schemes. The training and the validating sub-images come from different original images in order to ensure that the correlation of the colour texture images is minimized.

International audience; We present a new architecture to perform localization (position estimation) in GNSS systems, termed NEPS (Narrowband Efficient Positioning System). The NEPS archi- tecture is composed of three components: a low powered cheap receiver; a communication system which trans- mits the measurements; and a processing unit which receives the distorted observations (due to quantisation and imperfect transmission medium) and performs the position estimation algorithm. The NEPS is a stand-alone system which is designed to incorporate the quantised measurements as well as the imperfect communication channels between receiver and the backend in order to perform inference on the user's position. Compared with a conventional system, the NEPS consumes less band- width, requires lower power consumption and provides faster reporting rates. We derive the joint Maximum Likelihood (ML) for the position and the receiver's clock offset. We then develop an efficient algorithm to solve the resulting non-convex optimisation problem. Further- more, we derive a theoretical performance lower bound on the achievable accuracy via Crame ́r-Rao lower bound (CRLB). Simulate results show that the performance of the NEPS ML position estimator is close to the theoretical performance bound.

Distances in evidence theory are useful tools for belief function approximation or clustering. Efficient approaches are found in the literature, especially full metrics taking focal element interactions into account. In this paper, another aspect is investigated: the ability to detect common evidence pertaining to two different states of belief. This requirement, as well as the previously mentioned ones, are formalized through mathematical properties. To find a belief function distance satisfying the desired properties, matrix norms based distances between Dempsterian specialization matrices are investigated. It is proved that the L 1 Dempsterian matrix distance succeeds to fulfill all requirements. Interesting and unprecedented ties between the conjunctive combination rule and this distance are demonstrated. Several basic belief assignment distance experiments are proposed and interpreted thereby allowing to understand advantages and limitations of the newly introduced distances as compared to the existing ones.

International audience; In this Letter, we derive a general criterion for the physical realizability of the differential Mueller matrix m of a depolarizing continuous medium. The criterion is based on checking the positive semidefiniteness of a 3×3 restriction of the coherency matrix of the G-symmetric component of m and turns out be a close analogue of the well-known criterion of Cloude for a Mueller matrix to be physically realizable. The related aspects of local and global, as well as Stokes and physical (or Cloude), realizabilities are discussed comparatively. The practical application of the criterion, in connection with the above types of realizabilities, is illustrated on experimental Mueller matrices from the literature.

International audience; In this paper we are concerned by authentication of printer technologies from microscopic analysis of paper print. At this scale, a print is made of regularly spaced dots whose shape varies from a print to another and also inside the same document. Thus, dot at the microscopic scale can be considered as an intrinsic signature of printer technologies. Modeling and estimating such a signature for the authentication of printer technologies are really challenging. In this paper, we propose an original modeling of the micrometric scan of document printing. It consists in an extension of the binary response model which takes into account the dot shape. The digital image of a dot is therefore modeled as a set of random pixels distributed following to the so called inverse link function which depends on the center, tone of black, its spreading and its shape. A maximum likelihood estimation algorithm is provided in order to estimate the location, the darkness, the scale and shape parameter of the dot. From experimental results on three different printer technologies (inkjet, laser and offset), we show that the shape parameter is relevant for designing an identification scheme of printer technologies.

International audience; This article studies the impact of secure watermark embedding in digital images by proposing a practical implementation of secure spread-spectrum watermarking using distortion optimization. Because strong security properties (key-security and subspace-security) can be achieved using Natural Watermarking (NW) since this particular embedding lets the distribution of the host and watermarked signals unchanged, we use elements of transportation theory to minimize the global distortion (MSE). Next, we apply this new modulation, called Transportation Natural Watermarking (TNW), to design a secure watermarking scheme for grayscale images. TNW uses a multiresolution image decomposition combined with a multiplicative embedding which is taken into account at the distribution level. We show that the distortion solely relies on the variance of the wavelet subbands used during the embedding. In order to maximize a target robustness after JPEG compression, we select different combinations of subbands offering the lowest BERs for a target PSNR ranging from 35 to 55 dB and we propose an algorithm to select them. The use of transportation theory also provides an average PSNR gain of 3.6 dB on PSNR with respect to the previous embedding for a set of 2,000 images.

Publication; In this paper we introduce an evidential multi-source segmentation scheme for the extraction of prostate zonal anatomy using multi-parametric MRI. The Evidential C-Means (ECM) classifier was adapted to a segmentation scheme by introducing spatial neighbourhood-based relaxation step in its optimisation process. In order to do so, basic belief assignments on voxels membership were relaxed using distance-weighted combination of belief from spatial neighbours. For the application on prostate tissues, geometric a priori was modelled and used as an additional data source. Our method was first experimented on simulated images to prove the improvement brought to the ECM. A validation study of the segmentation method was then conducted on 31 patients MRI data. In order to take into account inter-observer variability, each MRI was manually segmented by three independent expert radiologists, and an estimated truth was computed using STAPLE algorithm. This validation proved that segmentation obtained with our method is accurate and comparable to expert segmentation. We also show that our segmentation scheme enables to detect and highlight outliers, which could be interpreted by physicians as irregular tissues. The use of belief functions also provides additional information on borders between structures. We do believe these are sources of evidence that could help physicians/algorithms in characterising tissues and structures. The method that is introduced in this paper is a step forward to the use of belief functions theory in the context of multi-source image segmentation. Nevertheless, a full comparison to both baseline (e.g. Gaussian Mixture Models) and recent (e.g. Graph Cut) segmentation methods is needed to assess its performance.